code: def parse_args():
      parser = argparse.ArgumentParser(description="Train face network")
      # general
      parser.add_argument(
          "--dataset", default=default.dataset, help="dataset config"
      )
      parser.add_argument(
          "--network", default=default.network, help="network config"
      )
      parser.add_argument(
          "--loss", default=default.loss, help="loss config"
      )
      args, rest = parser.parse_known_args()
      generate_config(args.network, args.dataset, args.loss)
      parser.add_argument(
          "--models-root",
          default=default.models_root,
          help="root directory to save model.",
      )
      parser.add_argument(
          "--pretrained",
          default=default.pretrained,
          help="pretrained model to load",
      )
      parser.add_argument(
          "--pretrained-epoch",
          type=int,
          default=default.pretrained_epoch,
          help="pretrained epoch to load",
      )
      parser.add_argument(
          "--ckpt",
          type=int,
          default=default.ckpt,
          help="checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save",
      )
      parser.add_argument(
          "--verbose",
          type=int,
          default=default.verbose,
          help="do verification testing and model saving every verbose batches",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=default.lr,
          help="start learning rate",
      )
      parser.add_argument(
          "--lr-steps",
          type=str,
          default=default.lr_steps,
          help="steps of lr changing",
      )
      parser.add_argument(
          "--wd", type=float, default=default.wd, help="weight decay"
      )
      parser.add_argument(
          "--mom", type=float, default=default.mom, help="momentum"
      )
      parser.add_argument(
          "--frequent", type=int, default=default.frequent, help=""
      )
      parser.add_argument(
          "--per-batch-size",
          type=int,
          default=default.per_batch_size,
          help="batch size in each context",
      )
      parser.add_argument(
          "--kvstore",
          type=str,
          default=default.kvstore,
          help="kvstore setting",
      )
      args = parser.parse_args()
      return args
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import math
  import random
  import logging
  import sklearn
  import pickle
  import numpy
  import mxnet
  from mxnet import ndarray
  import argparse
  import mxnet.optimizer
  from config import config, default, generate_config
  from metric import *
  import flops_counter
  import verification
  import fresnet
  import fmobilefacenet
  import fmobilenet
  import fmnasnet
  import fdensenet
  import vargfacenet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args